Most of current indoor positioning techniques use Wi-Fi, Bluetooth and the magnetic field of the earth for positioning. When an indoor positioning system is deployed, location fingerprints (which are measured with received signal strength indicators (RSSIs)) are required to be collected and processed at each sampling point for off-line training, a detection device is held and moved within a positioning area for on-line calibration to complete a signal map with the use of a learning algorithm, and then the signal map is compiled into an indoor map so as to synchronize with the indoor map.
However, the above described method not only has complicated prearrangement and data update processes, but also results in difficulty in accurately predicting a coverage of positioning devices due to signal blocking cause by building construction and interior arrangement. Moreover, changes of the positioning devices cannot be perceived, and even though locations of the positioning devices (wireless communication devices) are acquired in advance, it is difficult to establish a coordinate system to standardize reference frames of the indoor map.